How to use featuretools at the test time? It seems featuretools' feature definitions do not store train time statistics to accurately apply primitives like 'PERCENTILE' at the test time

Creating a github issue for better attention. I have a [StackOverflow question](https://stackoverflow.com/questions/78212699/how-to-use-featuretools-at-the-test-time#78212699) for the same as well

I would demonstrate the issue with an example:

Let us say we want to use the primitive 'PERCENTILE'

Imports:
```
import pandas as pd
import featuretools as ft
```

For training (create a simple data with one column and let featuretools compute a percentile feature on top of it):
```
df_train = pd.DataFrame({'index':[1,2,3,4,5], 'val':[1,2,3,4,5]})
es_train = ft.EntitySet("es_train")
es_train.add_dataframe(df_train,'df')
fm, fl = ft.dfs(entityset = es_train, trans_primitives=['percentile'], agg_primitives=[], target_dataframe_name='df')
```
output:
```
print(fm)
val PERCENTILE(val)
index
1 1 0.2
2 2 0.4
3 3 0.6
4 4 0.8
5 5 1.0
```

So far everything is expected

Now, when I get an example with the value, say, 3, at the test time. I would want it translated to 0.6 as per the training data. But, that is not what happens

```
df_test = pd.DataFrame({'index':[1], 'val':[3]})
es_test = ft.EntitySet("es_test")
es_test.add_dataframe(df_test,'df')
ft.calculate_feature_matrix(features = fl, entityset=es_test)
```

output:

```
val PERCENTILE(val)
index
1 3 1.0
```

So, metadata in feature definitions in ```fl``` that is the output of ```ft.dfs``` does not store train time stats needed to compute the features at the test time. This would throw any machine-learning model into a tailspin

What is the canonical way to apply featuretools at the test time?